Post on 02-Jul-2018
Berlin, 10/24/2017, 14:30 – 15:30
Tecnomatix Plant Simulation in the context of
research and development
B. Denkena, S. Wilmsmeier
Room: Estrel Hall B
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 2 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
1. The IFW
2. Research with Tecnomatix Plant Simulation – An overview
3. Practical example 1 – Plant Simulation as part of the digital factory
4. Practical example 2 – Employee competency based simulation
5. Summary
Content
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 3 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
1. The IFW
2. Research with Tecnomatix Plant Simulation – An overview
3. Practical example 1 – Plant Simulation as part of the digital factory
4. Practical example 2 – Employee competency based simulation
5. Summary
Content
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 4 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Associated Centers
Laser Zentrum
Hannover
Institut für Integrierte
Produktion Hannover
Leibniz Research
Center Energy 2050
Production Engineering at the Leibniz Universität Hannover
Photo: sliwonik.com
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 5 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Employees (PZH wide / IFW only)
Researchers: ca. 260 / 88
Technicians and administrations: ca. 100 / 20
Student assistants: ca. 500 / 211
Students: ca. 800
Machines and equipment
High-quality machine tools and installations
Latest measuring equipment, SEM, laboratories
Cleanroom (350 m2, class 100)
Building
Approx. 22,000 m2 effective surface for office buildings, proving
grounds, lecture and seminar rooms, library, cafeteria etc.
Infrastructure of the Center for Production Technology
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 6 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Institute of Production Engineering and Machine Tools
Manufacturing processes(Dr.-Ing. Thilo Grove)
Grinding technology
Cutting
Tailored surfaces
Machines and controls(Benjamin Bergmann)
Machine components
Machines and Monitoring
Production systems(Dr.-Ing. Marc-André Dittrich)
Production planning and control
Process planning and simulation
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 7 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Process planning and simulation
CAD / CAM process chain
NC programming
NC optimization
Development of CAM modules
Virtual control
Analysis of cutting conditions
Visualization of machine kinematics
Production planning and control
Integrated work planning and production control
Process chain optimization
Availability and maintenance
Skill-oriented planning
Technological simulation of the process chain
Interface solutions for coupled simulations
Sustainable production
Department production systems
5-axis simultaneous machining
Work planning and production control
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 8 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
1. The IFW
2. Research with Tecnomatix Plant Simulation – An overview
3. Practical example 1 – Plant Simulation as part of the digital factory
4. Practical example 2 – Employee competency based simulation
5. Summary
Content
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 9 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Planning Control Employees Modelling
Development of a
method for integrated
production and
maintenance
planning
Exploration of
interdisciplinary
planning approaches
Development of
algorithms for optimal
production control by
means of simulation
Exploring measures
of sequencing and
pooling
Development of a
method for
simulation-based
cost-benefit analysis
of training measures
Exploration of further
training potentials
Development of a
method for the fully
automated adaptation
of simulation models
by means of machine
data
Exploration of
adaptive simulation
models
Trainings
Selection of projects with Tecnomatix Plant Simulation
Checklist
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 10 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
1. The IFW
2. Research with Tecnomatix Plant Simulation – An overview
3. Practical example 1 – Plant Simulation as part of the digital factory
4. Practical example 2 – Employee competency based simulation
5. Summary
Content
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 11 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Planning Control Employees Modelling
Development of a
method for integrated
production and
maintenance
planning
Exploration of
interdisciplinary
planning approaches
Development of
algorithms for optimal
production control by
means of simulation
Exploring measures
of sequencing and
pooling
Development of a
method for
simulation-based
cost-benefit analysis
of training measures
Exploration of further
training potentials
Development of a
method for the fully
automated adaptation
of simulation models
by means of machine
data
Exploration of
adaptive simulation
models
Practical example 1
Checklist
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 12 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Problems in production and maintenance planning
Separate consideration of production and maintenance leads to inefficient use of
resources
Digitization offers the possibility to plan maintenance measures at an early stage
Impact of individual maintenance measures on production difficult to quantify
Static methods for production and maintenance planning do not show the
interactions
?
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 13 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Solution – Integrated production & maintenance planning
Time t
M1
M2
M3
M4
M5
A1
A3
A2
Setup changeoverReserved capacity for
production order Ai
Production-free period
(e. g. weekend)
A4
A3 A4
A1 A2
A2
Maintenance planning (variable)
Starting point of
maintenance measures tSj
Constant parameters
Batch sizes
Sequences of production orders
Machine assignments
Planning period T
t1
Maintenance measure MAj
Maintenance time tjStarting point of maintenance measures tSj
tS1
MA11
1
A1
Buffer Pl
Machine Mp
PS1
Process step
PSk
PS2
A1,
A2
A3,
A4
Machine
assignment
for production
order Ai
PS
1P
S2
Krö/68786 © IFW
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 14 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Solution – Integrated production & maintenance planning
Time interval ∆t
M3 A1 A2
tS1,mtS1,1 tS1,2 …
m simulation experiments,
n views per experiment
Time t
Planning period T
Observation time tB
MA1
M1 M2
M3 M4 M5
P1
P2 P3
P4
Krö/68788 © IFW
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 15 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
RF: Order variants
LG: Lot sizes
B: Machine utilisation variants
z: Maintenance start time
p: possible planning scenarios
Decision algorithm
Wil/91104 © IFW
Choose machine
utilisation variant B
Choose order
variant RF
Choose lot size
LG
Choose mainte-
nance start time tSj,z
Combine RF, LG,
B and tSj,z
Save planning
scenario p
Maintenance start
times processed?
Machine utilisation
variants processed?
Lot size vatriants
processed?
Order variants
processed?Reduction of
experimental plan
eliminate invalid
planning scenarios
eliminate not
sufficient planning
scenarios
eliminate scenarios
by company
specific „know-
how“
Simulate scenarios
yes
yes
yes
yes
no
no
no
no
p = p+1
B = 1
LG = 1
RF = 1
tSj,z = 1
B = B+1
LG = LG+1
RF = RF+1
z = z+1
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 16 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
0
2000
4000
6000
8000
10000
12000
14000
16000
0 8 16 24 32 40 48 56 64 72 80 88 96 104 112 120
Starting point of maintenance measure tsj
Weekend
Dyn
am
ic M
ain
ten
an
ce
co
sts
D
ire
ct m
ain
ten
an
ce
co
sts
Ind
ire
ct M
ain
ten
an
ce
co
sts
[€*]I II III IV
DynamicMaintenance costs
DirectMaintenance costs
Indirect Maintenance costs
[h]
*Values have been removed due toconfidentiality
ΔK
I. Constant
Maintenance time tj : 12 h
Observation period tB : 120 h
Time interval ∆t : 2 h
Confidence interval : 90 %
Legend
Production-free period
(weekend)
Setup changeover
Maintenance measure
II. Variable
Starting point of
maintenance measure tsj
A4A2 A3
Observation period tB
Time t
A1
Krö/68791 © IFW
Simulation results first research phase
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 17 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Summary first research phase
Dynamic input
Order variant
Lot size
Machine utilisation variant
Maintenance start time
Static input
Layout related information
Machine behaviour
Output
Optimal production
and maintenance plan
Reduction of unit costs
by up to 7 %
Development of a method for the automated adaptation of simulation models by means of
machine data
Further need for research
Continious manual
adaption needed for
application during
operation time
Dynamic
planning method
Simulation model
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 18 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Simulation model adaption with machine data acquisition
Decreasing accuracy of the simulation models in use with increasing deployment time
due to changed plan data base
Adaptation of the simulation models time-, personnel- and cost-intensive
Plan data base is updated at the beginning of the simulation
Data base improves with increasing deployment time (edited historical data)
Manual adaptation only necessary for structural changes
Time
Model
validitySelf-parameterizing and
learning simulation model
Traditional
simulation model
Model
creation
Model
usage
Adaption
„Target“
validity
Wil/86787 © IFW
Traditional simulation models
Self-parameterizing and learning simulation models
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 19 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Integration of machine data in material flow simulation
MDE/BDEMDE/BDEMDE/BDE
MDE/BDE
Evaluation
algorithms
time period
related data
Adaptive simulation model
Wil/86761 © IFW
Interfaces (selection)
OPC-DA/UA,
DDE, MCIS, DNC,
FOCAS 1&2,
NIO Interface,
Open Core Interface,
Modbus
Protocols (selection)
FTP, TCP
SQL
Web Applikation
SQL query
Create
XML files
time
related data
MDE/BDE
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 20 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Data preparation and automated model adaption
Simulation model
Tests of goodness
of fit
DataFit
Calculate
percentage
distribution
Process
data
Wil/86798 © IFW
Transfer of
results
Machine 1
95 %2 %3 %
Machine 1
Dura
tio
n
Dis
tan
ce
Data logger
ok
wa
ste
rew
ork
ing
Fre
qu
en
cy
Stored
data
Machine 1
Failure
duration
1000
80
5680
...
...
9500
7644
Failure
distance
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 21 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Adaptive and integrated
maintenance and production planning
Simulation-based decision supportWeb-based acquisition and visualisation
Simulation model
Production
planningMaintenance
planning
Real production system
1 Plan data, real data from MDC and PDC systems
2
2
Selected real data
3 Prepared database (e.g. cycle and setup time,
machine failure behaviour)
5
4
Simulation results (e.g. failure follow-up costs)
Decision for real system
4
5
Data preparation
1
3
Wn/72107 © IFW
0
1000
2000
3000
4000
5000
6000
7000
8000
2 10 18 26 34 42 50 58 66 74 82 90 98 106[h]
Maschine 2
Startzeitpunkt IH-Maßnahme
Maschine 1
Maschine 3
[€]
Au
sfa
llfo
lge
ko
ste
n Wochenende
Startzeitpunkt Instandhaltungsmaßnahme
Ausfa
llfo
lge
ko
ste
n
Data acquisition and selection
Visualisation of results
Legend
Time of maintenance measure
Failu
re f
ollo
w-u
p c
osts
Machine 1 Machine 2
Machine 3
Weekend
0
7
14
21
28
5 10 15 20 25 30 35 40 45 50 55 60
Fre
qu
en
cy
Failure distance
Histogram short-term failure distance
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 23 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Simulation model
Summary second research phase
Simulation model
Dynamic Input
Order variant
Lot size
Machine utilisation variant
Maintenance start time
Machine behaviour &
system status
Static Input
Layout related information
Output
Optimal production
and maintenance plan
Valid simulation model without cost
and time consuming manual efforts
Shortening of the settling phase
due to initialized buffers
Development of a method for the (fully) automated creation of simulation models based on
layout scans
Requested research project
Temporarily manual
adaption needed if
layout changes
Dynamic
planning method
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 24 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
1. The IFW
2. Research with Tecnomatix Plant Simulation – An overview
3. Practical example 1 – Plant Simulation as part of the digital factory
4. Practical example 2 – Employee competency based simulation
5. Summary
Content
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 25 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Planning Control Employees Modelling
Development of a
method for integrated
production and
maintenance
planning
Exploration of
interdisciplinary
planning approaches
Development of
algorithms for optimal
production control by
means of simulation
Exploring measures
of sequencing and
pooling
Development of a
method for
simulation-based
cost-benefit analysis
of training measures
Exploration of further
training potentials
Development of a
method for the fully
automated adaptation
of simulation models
by means of machine
data
Exploration of
adaptive simulation
models
Practical example 1
Checklist
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 26 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Stable connection between the competence development of employees and
innovative ability of companies
Benefit of trainings in companies can only be estimated
Mathematical models allow a quantitative description of the entire workforce
performance of a company
Individual employees or their characteristics and abilities are not explicitly taken
into account
If companies are able to flexibly vary key decision-making variables, companies can
then develop their business and situation-specific optimal training strategy.
Initial considerations for the SAPA project
Initial hypothysis
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 27 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
System behavior of a production system is often too complex for it to be fully
captured and evaluated
Material flow simulations represent the system behavior of a production in a
model (using appropriate software)
Employee competencies are elements of a production system and can be
represented in the material flow simulation
If employee competencies are represented, a change in competencies (eg. by
trainings) leads to a change in the production system
Thus planning processes can be supported or optimized, by using simulation
approaches
Competence-based simulation
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 28 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Example: Material flow simulation
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 29 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Example: Production program
0
2
4
6
8
10
12
14
Quantity
of pro
ducts
Simulation time
Product 1
-
Product 2 Product 3
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 30 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
0
2
4
6
8
10
12
14
16
Quantity
of pro
ducts
Simulation time
Product 1
Output
-
Example: Reaction of the production system
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 31 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Example: Reaction of the production system
0
2
4
6
8
10
12
14
16
18
Quantity
of pro
ducts
Simulation time
Stock
-
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 32 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
0
2
4
6
8
10
12
14
16
Quantity
of pro
ducts
Simulation time
-
Product 1
Stock
Output
Example: Further training of employee 1
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 33 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Example: Further training of employee 2
0
2
4
6
8
10
12
14
16
18
Quantity
of pro
ducts
Simulation time
-
Product 1
Stock Output
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 34 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
1. The IFW
2. Research with Tecnomatix Plant Simulation – An overview
3. Practical example 1 – Plant Simulation as part of the digital factory
4. Practical example 2 – Employee competency based simulation
5. Summary
Content
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 35 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
The IFW uses Tecnomatix Plant Simulation for research and improvement of
production systems
Production planning and control, the employees and simulation techniques can
be investigated with Plant Simulation
Digitization allows the integration of Tecnomatix Plant Simulation as part of the
Digital Factory into the daily planning processes
Practical implementations of developed methods show the reproducibility of
simulated results
Summary
Planning Control Employee Technique
Tecnomatix Plant Simulation
Checklist
© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena
Seite 36 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier
Thank you for your attention!